四驅越野車轉向驅動橋的設計含8張CAD圖

四驅越野車轉向驅動橋的設計含8張CAD圖,越野車,轉向,驅動,設計,cad Artificial Intelligence Application’s for 4WD Electric Vehicle Control SystemKeywords4WD; PI; Adaptive Fuzzy PI; Fuzzy Controller; Direct Torque Control.SOCABSTRACTA novel speed control design of 4WD electric vehicle (EV) to improve the comportment and stability under different road constraints condition is presented in this paper. The control circuit using intelligent adaptive fuzzy PI controller is proposed. Parameters which guide the functioning of PI controller are dynamically adjusted with the assistance of fuzzy control. The 4WD is powered by four motors of 15 kilowatts each one, delivering a 384 N.m total torque. Its high torque (338 N.m) is instantly available to ensure responsive acceleration performance in built-up areas. The electric drive canister of tow directing wheels and tow rear propulsion wheels equipped with tow induction motors thanks to their light weight simplicity and their height performance. Acceleration and steering are ensure by electronic differential, the latter control separately deriving wheels to turn at any curve. Electric vehicle are submitted different constraint of road using direct torque control. Electric vehicle are simulated in Matlab Simulink. The simulation results have proved that the intelligent fuzzy PI control method decreases the transient oscillations and assure efficiency comportment in all topologies road constraints, straight, curved road, descent.1. IntroductionThe principal constraints in vehicle design for transportation are the development of a non-polluting high safety and comfortable vehicle. Taking into account these constraints, our interest has been focused on the 4WD electrical vehicle, with independent driving wheel-motor at the front and with classical motors on the rear drive shaft [1-4]. This configuration is a conceivable solution, the pollution of this vehicle is strongly decreased and electric traction gives the possibility to achieve accurate and quick control of the distribution torque. Torque control can be ensured by the inverter, so this vehicle does not require a mechanical differential gear or gearbox. One of the main issues in the design of this vehicle (without mechanical differential) is to assume the car stability. During normal driving condition, all drive wheel system requires a symmetrical distribution of torque in the both sides. In recent years, due to problems like the energy crisis and environmental pollution, the Electric Vehicle (EV) has been researched and developed more and more extensively [1,2]. Currently, most EVs are driven by two front wheels or two rear wheels. Considering some efficiency and space restrictions on the vehicle, people have paid more and more attention in recent years to fourwheel drive vehicles employing the IM in-wheel motor.Research has shown that EV control methods such as, PI control are able to perform optimally over the full range of operation conditions and disturbances and it is very effective with constant vehicle torque, Moreover these non-linear vehicle torque are not fixed and change randomly. However EV with conventional PI control may not have satisfactory performance in such fast varying conditions, the system performance deteriorates. In addition to this, it is difficult to select suitable control parameters Kp and Ki in order to achieve satisfactory compensation results while maintaining the stability of EV traction, due to the highly complex, non-linear nature of controlled systems. These are two of the major drawbacks of the PI control. In order to overcome these difficulties, adaptive PI controller by fuzzy control has been applied both in stationary and under roads constraints, and is shown to improve the overall performance of 4WD electric vehicle.The aim of this paper is to understand the impact of intelligent fuzzy speed controller using lithium-ion battery controlled by DC-DC converter, each wheels is controlled independently by via direct torque control based space vector modulation under several topologies. Modelling and simulation are approved out using the Matlab/Simulink tool to study the performance of 4WD proposed system.2. Electric Vehicle DescriptionAccording to Figure 1 the opposition forces acting to the vehicle motion are: the rolling resistance force due to the friction of the vehicle tires on the road; the aerodynamic drag force caused by the friction on the body moving through the air; and the climbing force that depends on the road slope.The total resistive force is equal to and is the sum of the resistance forces, as in (1).(1)The rolling resistance force is defined by:(2)The aerodynamic resistance torque is defined as follows:(3)The rolling resistance force is usually modeled as:(4)where r is the tire radius, m is the vehicle total mass, is the rolling resistance force constant, g the gravity acceleration, is Air density, is the aerodynamic drag coefficient, is the frontal surface area of the vehicle, v is the vehicle speed, is the road slope angle. Values for these parameters are shown in Table 1.3. Direct Torque Control StrategyThe basic DTC strategy is developed in 1986 by Takahashi. It is based on the determination of instantaneous space vectors in each sampling period regarding desired flux and torque references. The block diagram of the original DTC strategy is shown in Figure 2. The reference speed is compared to the measured one. The obtained error is applied to the speed regulator PI whose output provides the reference torque. The estimated stator flux and torque are compared to the corresponding references. The errors are applied to the stator flux and torque hysteresis regulators, respectively. The estimation value of flux and its phase angle is calculated in expression(5)(6)And the torque is controlled by three-level Hysteresis. Its estimation value is calculated in expression (7).(7)Figure 1. The forces acting on a vehicle moving along a slope.Figure 2. PI gains online tuning by fuzzy logic controller.Table 1. Parameters of the electric vehicle model.4. Intelligent Fuzzy PI ControllerFuzzy controllers have been widely applied to industrialprocess. Especially, fuzzy controllers are effective techniques when either the mathematical model of the system is nonlinear or no the mathematical model exists. In this paper, the fuzzy control system adjusts the parameter of the PI control by the fuzzy rule. Dependent on the state of the system. The adaptive PI realized is no more a linear regulator according to this principle. In most of these studies, the Fuzzy controller used to drive the PI is defined by the authors from a series of experiments [5-8].The expression of the PI is given in the Equation (2).(8)where:: Output of the control; : Input of the control. The error of the reference current and the injected speed ; Parameter of the scale; Parameter of the integrator.The discrete equation:(9)where:: Output on the time of k th sampling; : Error on the time of k sampling; Cycle of the samplingOn-line Tuning:The on-line tuning equation for kp and ki are shown below:(10)(11)The frame of the fuzzy adaptive PI controller is illustrated in Figure 2.The linguistic variables are defines as {NL, NM, NS, Z, PS, PM, PB} meaning negative large, negative medium, negative small, zero, positive small, positive medium, positive big.The Membership function is illustrated in the Figures 3-6.The view plot surface of fuzzy controller for kp and ki are shown in Figures 7 and 8 respectively.Table 2 shows the fuzzy tuning rules.Table 2. Fuzzy tuning rules.Figure 3. The membership function of input e(k).Figure 4. The membership function of input Δe(k).kpFigure 5. The membership function of output kp.kiFigure 6. The membership function of output ki.Figure 7. View plot surface of fuzzy controller for kp.Figure 8. View plot surface of fuzzy controller for ki.5. Simulation ResultsIn order to analyze the driving wheel system behavior, Simulations were carried using the model of Figure 9. The following results were simulated in MATLAB and its divided in two phases. The first one deal with the test of the EV performances controlled with DTC strategy under several topology variation in the other hand we show the impact of this controller on vehicle power electronics performances. Only the right motor simulations are shown. The assumption that the initialized lithiumion battery SOC is equal to 70% during trajectories.5.1. Intelligent Fuzzy PI Controller for Direct Torque Control SchemeThe topology studied in this present work consists of three phases: the first one is the beginning phase’s with speed of 80 Km/h in straight road topology, the second phase present the curved road with the same speed, finally the 4WD moving up the descent road of 10% under 80 Km/h, the specified road topology is shown in Figure 10, when the speed road constraints are described in the Table 3.Refereed to Figure 11at time of 2 s the vehicle driver turns the steering wheel on a curved road at the right side with speed of 80 Km/h, the assumption is that the four motors are not disturbed. In this case the front and rear driving wheels follow different paths, and they turn in the same direction but with different speeds. The electronic differential acts on the four motor speeds by decreasing the speed of the driving wheel on the right side situated inside the curve, and on the other hand by increasing the wheel motor speed in the external side of the curve. The behaviors of these speeds are given in Figure 11. At t = 3 s the vehicle situated in the second curve but in the left side, the electronic differential compute the novel steering wheels speeds references in order to stabilize the vehicle inside the curve. The battery initial SOC of 70 % is respected. In this case the driving wheels follow the same path with no overshoot and without error which can be justified with the good electronic differential act coupled with DTC performances.Figures 12-15 show the variation of kp and ki of the four intelligent speed controller.Figure 16 describes the variation of current for the front motor right in different phases. In the first step and to reach 80 Km/h, the EV demands a current of 48.75 A for each motors which explained with electromagnetic torque of 138.20 N.m. In the curved road the current and electromagnetic torque demand are computed using the electronic differential process according to the driver decision by means that the speed reference of each wheels is given by the electronic differential computations witch convert the braking angle in the curve on linear speeds. Figure 17 shows the electromagnetic torque of the front motor right. The third phase explains the effect of the descent slopped road the electromagnetic torque decrease and the current demand undergo half of the current braking phases. The presence of descent causes a great decrease in the phase current of each motor by means that the sloped force became an motor force. They develops approximately 96.17 N.m each one. The linear speeds of the four induction motors stay the same and the descent sloped road does not influence the torque control of eachFigure 9. The driving wheels control system.Figure 10. The chosen road topology of tests.Figure 11. Variation of vehicle speeds in different phases.Figure 12. Variation gain kp of intelligent fuzzy PI for the front right and left speed controller.Figure 13. Variation gain ki of intelligent fuzzy PI for the front right and left speed controller.Figure 14. Variation gain kp of intelligent fuzzy PI for the rear right and left speed controller.Figure 15. Variation gain ki of intelligent fuzzy PI for the rear left and left speed controller.Figure 16. Variation of phase current of the front motor right in different phases.Figure 17. Variation of electromagnetic torque of the front motor right in different phases.Table 3. The driving road topology description.wheels. The results are listed in Table 4.According to the formulas (1), (2), (3) and (4) and Table 4, the vehicle resistive torque was 127.60 N.m in the first case (beginning phase) when the power propulsion system resistive one is 127.60 N.m in the curved road. The driving wheels develop more and more efforts to satisfy the traction chain demand which justify a resistive torque equal to 127.60 N.m in the third descent slopped phase. The result prove that the traction chain under descent demand develop less effort comparing with the breaking phase case’s by means that the vehicle needs the half of its energy in the descent sloped phase’s compared with the sloped one’s as it specified in Table 5 and Figure 17.5.2. Power ElectronicsThe Lithium-ion battery must be able to supply sufficient power to the EV in accelerating and decelerating phase, which means that the peak power of the batteries supply must be greater than or at least equal to the peak power of the both electric motors. The battery must store sufficient energy to maintain their SOC at a reasonable level during driving, the Figure 18, describe the changes in the battery storage power in different speed references.Table 4. Values of phase current driving force of the right motor in different phases.Table 5. Variation of vehicle torque in different phases.Figure 18. Variation of Lithium-ion battery power in different phases.It is interesting to describe the power distribution in the electrical traction under several speed references as it described in Table 6. The battery provides about 20.73 Kw in the first phase in order to reach the electronic differential reference speed of 80 Km/h. In the second phase (phase 2: curved phase’s) the demanded power battery stay the same which present amount of 66.87% of the globally nominal power battery (31 Kw). In third phase the battery produced power equal to 13.73 Kw under descent slopped road state. The battery produced power depend only on the electronic differential consign by means the courved and descente sloped road driver state which can be explained by the battery SOC of Figure 19.Figure 19 explains how SOC in the Lithium-ion battery changes during the driving cycle; it seems that the SOC decreases rapidly at acceleration, by means that the SOC range’s between 68.44% to 70% during all cycle’s phases from beginning at the end cycles.At t = 4 s, the battery SOC becomes lower than 68.44% (it was initialized to 70% at the beginning of the simulation).Table 7 reflects the variation of SOC in different simulations phases. The relationship between SOC and left time in three phases are defined by the flowing linear fitting formula:(12)Table 6. Variation of battery power in different trajectory phases.Table 7. Evaluation of SOC [%] in the different phases.Figure 19. Battery efficiency versus state-of-charge.Moreover the simulation results specified by Figure 20, we can define the relationship between the sate of charge and the traveled distance in each cases as it shown in Table 8 and the relationship between power consumed and state of charge during each phase as it shown in Table 9, the first one (beginning phase) is defined by the linear fitting formula:(13)This power is controlled by the Buck Boost DC-DC converter current and distribute accurately for three phases. Figure 21 shows the buck boost DC-DC converter robustness under several speed cycles. The buck boost converter is not only a robust converter which ensures the power voltage transmission but also a good battery recharger in deceleration state that help to perfect the vehicle autonomous with no voltage ripple.6. ConclusionThe research outlined in this paper has demonstrated theFigure 20. Evaluation traveled distance en function the SOC.Figure 21. Buck boost DC-DC converter behavior under several speed variations.Table 8. Evaluation of distance traveled and SOC.Table 9. The relationship between the traction chain power electronics characteristics and the distance traveled in differ-ent phases.feasibility of improved vehicle stability for 4WD electric vehicle using DTC controls. DTC with intelligent fuzzy speed controller is able to adapt itself the suitable control parameters which are the proportional and integral gains and ki to the variations of vehicle torque. This method was improved proposed traction system steering and stability during different trajectory this. The advantage DTC controller is robustness and performance, there capacity to maintain ideal trajectories for four wheels control independently and ensure good disturbances rejections with no overshoot and stability of vehicle perfected ensured with the speed variation and less error speed. The 4WD electric vehicle was proved best comportment and stability during different road path by maintaining the motorization error speed equal zeros and gives a good distribution for electromagnetic torque. The electric vehicle was proved efficiency comportment under different road topologies.四驅越野車控制系統(tǒng)人工智能應用關鍵詞：四驅，PI，自適應模糊 PI（比例—積分）,模糊控制器，直接轉矩控制。摘要本文提出了在不同道路約束條件下，一種新型四驅電動車的速度控制設計如何提高其準確性和穩(wěn)定性。提出了使用智能自適應模糊 PI 控制器的控制電路。引導 PI 控制器功能的參數在模糊控制器幫助下的動態(tài)調整。4WD 由每臺 15KW 的 4 臺電機提供動力，總扭矩為384N·m。其高扭矩（384N·m）的立即可用確保在組合區(qū)域相應加速性能。由于質輕簡單和高性能，牽引轉向輪和后牽引運動輪的電力驅動罐裝備了牽引感應發(fā)動機。通過電子差速器確保加速和轉向，后者控制單獨導出以任何角度轉動的車輪。電動車輛使用直接轉矩控制提供不同的道路約束。電動汽車在 Matlab Simulink 中模擬。模擬結果證明，智能模糊 PI 控制方法減少了瞬時震蕩，并確保了所有拓撲中道路約束，直線，彎曲，下降的效率。1.介紹運輸車輛設計的主要制約因素是無污染的高安全性和舒適性車輛的開發(fā)。考慮到這些限制，我們的興趣集中在 4WD 電動車輛，前置獨立的驅動輪電機和后置驅動軸上的傳統(tǒng)電機上。這種配置是可以想到的解決方案，車輛的污染被大大減少，電牽引給出了實現對分配扭矩的準確和快速控制的可能性。變頻器可以確保扭矩控制，因此該車輛不需要機械差速齒輪或變速箱。這種車輛（無機械差速）設計的主要問題是保證汽車的穩(wěn)定性。在正常行駛狀態(tài)下，所有的驅動輪系統(tǒng)在兩側都需要扭矩對稱分布。近年來，由于能源危機和環(huán)境污染等問題，電動汽車（EV）的研究和開發(fā)越來越廣泛。目前，大多數電動汽車是前輪驅動或后輪驅動。考慮到車輛的效率和空間限制，近年來人們越來越重視 IM 輪內動力四輪驅動車輛的使用。研究表明，諸如 PI 控制的 EV 控制方法能夠在整個操作條件和干擾范圍內進行最佳動作，并且在車輛轉矩恒定方面非常有效，而且這些非線性車輛轉矩不是固定的，而是隨機變化的。然而具有常規(guī) PI 控制的 EV 可能在這種快速變化，系統(tǒng)性能惡化的條件下不具有令人滿意的效果。除此之外，由于控制系統(tǒng)非常復雜，非線性的特性，想要達到滿意的補償結果，同時維持 EV 牽引器的穩(wěn)定性，所以選擇穩(wěn)定控制參數 Kp 和 Ki 很困難。這就是 PI控制的兩個主要缺點。為了克服這些缺點，通過模糊控制的自適應 PI 控制器已經在靜止和道路限制下應用，并且顯示出 4WD 電動車整體性能的提高。本文的目的是了解在使用 DC-DC 轉換器控制鋰離子電池對智能模糊速度控制器的影響，在多個拓撲下，每個車輪基于空間向量調制通過直接轉矩獨立控制控制。使用Matlab/Simulink 工具批準的建模和仿真，以研究 4WD 提出的系統(tǒng)性能。2.電動汽車概述根據圖 1，作用于車輛運動的反作用力的是：由于汽車輪胎與地面的摩擦所產生的滾動阻力 Ftire，車身移動過程中與空氣摩擦所產生的空氣動力學阻力 Faero 和由于道路坡度產生的攀爬力 Fslope?？傋枇Φ扔?Fr，并且是各阻力的總和，如（1）所示。（1）滾動阻力由下式定義：（2 ）氣動阻力矩定義如下：（3）滾動阻力通常建模為：（4 ）其中 r 是輪胎半徑，m 是車輛總質量，f r 是滾動阻力常數，g 是重力加速度，ρ air 是空氣密度，c d 是氣動阻力系數， Af 是車輛的正面面積，v 是車速， β 是道路坡度角。這些參數的值如表 1 所示。3.直接轉矩控制策略基本直接轉矩控制策略是 Takahashi 在 1986 年開發(fā)的。它基于每個采樣周期中關于期望的通量和轉矩參考的瞬時空間矢量的確定。原始 DTC 策略的框圖如圖 2 所示。將參考速度與測量速度進行對比。所獲得的誤差被應用到輸出提供參考扭矩的速度調節(jié)器 PI。將估計的定子磁通和轉矩與相應的參考值相比較。誤差分別應用于定子磁鏈和轉矩磁質調節(jié)器。在表達式中計算磁鏈和相位角的估計值，且轉矩由三電平滯環(huán)控制。其估計值由表達式（7）計算。（5）（6）（7）圖 1：沿斜坡行駛的車輛上的作用力圖 2：PI 控制器通過模糊邏輯控制器的在線調整表 1：電動汽車模型的參數4.智能模糊 PI 控制器模糊控制器已經廣泛地應用到了工業(yè)過程中了。特別地，當系統(tǒng)的數學模型是非線性的或者沒有數學模型存在時，模糊控制器是有效的技術手段。在本文中，模糊控制系統(tǒng)通過模糊規(guī)則來調整 PI 控制參數。由于提高系統(tǒng)的穩(wěn)定性這一原則，自適應 PI 不再是一個線性調節(jié)器。在大量研究中，驅動 PI 的模糊控制器是作者從一系列的試驗中確定下來的。PI 的表達式由等式 2 給出。（8 ）其中：y（t）：控制輸出；e（t）：控制輸入；參考電流 w’（t）和注入速度 w（t ）的誤差；K p：刻度參數；T i：積分器參數。離散方程：（9）其中：y（k）：第 k 次采樣時間的輸出；e（k ）：k 的采樣時間誤差；T：采樣周期。在線調整：kp 和 ki 的在線調整方程如下所示：（10）（11）模糊自適應 PI 控制器的框架如圖 2 所示。語言變量的定義是{NL, NM, NS, Z, PS, PM, PB}，其意義為負大，負中，負小，零，正小，正中，正大。參數功能如圖 3-6 所示。kp 和 ki 模糊控制器的視圖曲面分別如圖 7 和圖 8 所示。圖 2 表明了模糊調整規(guī)則。表 2:模糊調整規(guī)則圖 3：輸入 e（k）的函數圖像圖 4：輸入 Δe（k）的函數圖像圖 5：輸入 kp 的函數圖像圖 6：輸入 ki 的函數圖像圖 7：k p 模糊控制器的曲面視圖圖 8：k i 模糊控制器的曲面視圖5.仿真結果為了分析驅動輪系統(tǒng)的運動，采用圖 9 的模型進行仿真。在 MATLAB 中模擬了以下結果，且分為兩個階段。第一個處理在幾個拓撲變化下用 DTC 策略控制的 EV 性能測試，另一方面我們顯示了該控制器對車輛電力電子性能的影響。只顯示正確的電機模擬。假設在軌跡中初始化的鋰離子電池 SOC 等于 70%。5.1.用于直接轉矩控制的智能模糊 PI 控制器當前工作研究的拓撲結構由三個階段組成:第一階段是直路拓撲中四驅車以速度為 80 Km/h 行駛的起始階段，第二階段是相同速度的上下坡階段，最后是速度低于 80 Km/h，且坡度為 1:10 的下坡階段。道路中速度約束由表 3 所示，指定的道路拓撲如圖 10 所示。根據圖 11，假設四個電機不受干擾，在第二秒的時候，汽車駕駛員以 80 Km/h 的速度在彎道右側轉動方向盤。在這種情況下，前后驅動輪行駛路徑不同，雖然朝著相同的方向，但轉速不同。電子差速器通過降低位于曲線內側的右側驅動輪速度以及提高曲線外側的右側驅動輪速度來控制四個電機的速度。它們的速度變化如圖 11 所示。在 t=3s 時，位于第二曲線左側的車輛，電子差速器計算新的方向盤速度參考值，以使車輛在曲線內部趨于穩(wěn)定。電池初始 SOC 為 70%。在這種情況下，通過與 DTC 性能相結合的良好電子差動證明，驅動輪路徑相同沒有過沖和錯誤。圖 12-15 表明了四個智能速度控制器參數 kp 和 ki 的變化。圖 16 描述了不同階段前置電機電流的變化。在第一步車速達到 80 Km/h，電動機要求每個電機的電流為 48.75A，電磁轉矩為 138.20N·m。在彎道中，根據駕駛員決定，使用電子差速過程來計算電流和電磁轉矩需求，這意味著每個車輪的速度基準由電子差動計算開關給出，以線性速度轉換曲線中的制動角度。圖 17 顯示了前置電機的電磁轉矩。第三階段說明了斜坡下坡的影響，電磁轉矩